PURPOSE: The objective of this study was to identify differentially expressed proteins of advanced gastric cancer from patients with different prognosis using NanoLC-MS/MS (LTQ) (nanoflow liquid chromatography system interfaced with a linear ion trap LTQ mass spectrometer). METHODS: Eight gastric cancer patients with relatively early TNM stage and survival time >34 months were identified as good survival (group G), while the other eight with late stage and survival time <15 months as poor survival (group P). The total protein of the tissue samples from each group was extracted and pooled together respectively. The resulting two protein mixtures were trypsin-digested and analyzed using NanoLC-MS/MS (LTQ). Database searches were done against NCBI non-redundant database and SWISS-PROT database and the identified proteins were classified through an online Web Gene Ontology Annotation Plot tool. Immunohistochemistry was used to verify candidate prognosis-related proteins. RESULTS: There were 284 and 213 proteins identified for group G and group P respectively. And 117 proteins were detected exclusively in group G and 46 proteins exclusively in group P. These protein markers function in calcium ion signaling pathway, cellular metabolism, cytoskeleton formation, stress reaction, etc. Among those, the down-regulated expression of S100P was verified to claim a poor clinical outcome of gastric cancer patients (P = 0.0375). CONCLUSION: The MS-based proteomics approach is efficient in identifying differentially expressed proteins in relation to prognosis of advanced gastric cancer patients. These differentially expressed proteins could be potential prognosis-related cancer markers and deserve further validation and functional study.
PURPOSE: The objective of this study was to identify differentially expressed proteins of advanced gastric cancer from patients with different prognosis using NanoLC-MS/MS (LTQ) (nanoflow liquid chromatography system interfaced with a linear ion trap LTQ mass spectrometer). METHODS: Eight gastric cancerpatients with relatively early TNM stage and survival time >34 months were identified as good survival (group G), while the other eight with late stage and survival time <15 months as poor survival (group P). The total protein of the tissue samples from each group was extracted and pooled together respectively. The resulting two protein mixtures were trypsin-digested and analyzed using NanoLC-MS/MS (LTQ). Database searches were done against NCBI non-redundant database and SWISS-PROT database and the identified proteins were classified through an online Web Gene Ontology Annotation Plot tool. Immunohistochemistry was used to verify candidate prognosis-related proteins. RESULTS: There were 284 and 213 proteins identified for group G and group P respectively. And 117 proteins were detected exclusively in group G and 46 proteins exclusively in group P. These protein markers function in calcium ion signaling pathway, cellular metabolism, cytoskeleton formation, stress reaction, etc. Among those, the down-regulated expression of S100P was verified to claim a poor clinical outcome of gastric cancerpatients (P = 0.0375). CONCLUSION: The MS-based proteomics approach is efficient in identifying differentially expressed proteins in relation to prognosis of advanced gastric cancerpatients. These differentially expressed proteins could be potential prognosis-related cancer markers and deserve further validation and functional study.
Authors: Sally E Dowen; Tatjana Crnogorac-Jurcevic; Rathi Gangeswaran; Mikkel Hansen; Jyrki J Eloranta; Vipul Bhakta; Teresa A Brentnall; Jutta Lüttges; Gunther Klöppel; Nick R Lemoine Journal: Am J Pathol Date: 2005-01 Impact factor: 4.307
Authors: K Emoto; H Sawada; Y Yamada; H Fujimoto; Y Takahama; M Ueno; T Takayama; H Uchida; K Kamada; A Naito; S Hirao; Y Nakajima Journal: Anticancer Res Date: 2001 Mar-Apr Impact factor: 2.480
Authors: Noriyoshi Fukushima; Norihiro Sato; Nijaguna Prasad; Steven D Leach; Ralph H Hruban; Michael Goggins Journal: Oncogene Date: 2004-12-02 Impact factor: 9.867
Authors: Chi-ho Lee; John Hon-kei Lum; Belinda Pik-yuen Cheung; Man-sau Wong; Yoki Kwok-chu Butt; Ming F Tam; Wing Y Chan; Chit Chow; Pak-kwan Hui; Francis S L Kwok; Samuel Chun-lap Lo; D M Fan Journal: Proteomics Date: 2005-03 Impact factor: 3.984
Authors: Joao A Paulo; Linda S Lee; Bechien Wu; Kathryn Repas; Peter A Banks; Darwin L Conwell; Hanno Steen Journal: Proteomics Clin Appl Date: 2010-09 Impact factor: 3.494